Linear precoding for mimo channels with outdated channel state information in multiuser space-time block coded systems with multi-packet reception

ABSTRACT

A joint set of linear precoder designs is provided for single cell uplink multiuser space-time block coded multiple-input multiple-output (MIMO) systems with multi-packet reception by exploiting outdated channel state information. By deriving the pairwise error probability with respect to both minimum and average codeword distance design metrics, the technique solves an optimization problem subject to transmit power constraint for each user and dependent on the outdated channel state information. Due to the non-convex nature of the optimization problem, an iterative technique based on alternating minimization and projected gradient can be used to solve for a joint linear preceding structure for general space-time block coding. The linear precoding structure is then sent from the base station to various consumer premise equipment for use in later transmissions. For orthogonal space-time block code, a simplified distributed technique is provided to solve for a closed-form solution of the optimization problem.

TECHNICAL FIELD

The subject disclosure relates generally to wireless communications systems, and more particularly to precoder designs for multiuser space-time block coded systems with multi-packet reception.

BACKGROUND OF THE INVENTION

Wireless communication networks are increasingly popular and widely deployed. Conventionally, however, wireless communication allows a single transmission at a given frequency at the same time. Thus, frequency/time division duplexing is used to allow multiple users to transmit information in a wireless communication network. However, this leads to a reduced data rate for a given channel bandwidth.

Multiple-input multiple-output (MIMO) technology is a promising candidate for next-generation wireless communications. In order to achieve a high data rate over MIMO channels, space-time codes, which perform coding across both spatial and temporal dimensions, can be utilized to maximize possible diversity and coding advantages without sacrificing channel bandwidth. However, channel state information (CSI) is not required in space-time code design.

In traditional frequency/time division duplex systems where CSI can be fed back/estimated, CSI can actually be exploited for optimum or quasi-optimum precoder and equalizer designs with the purpose of maximizing system performance. Nonetheless, since CSI is not required in space-time codes, often only limited CSI information can be available and/or the CSI can be outdated due to feedback delay. Consequently, CSI is traditionally not exploited to optimize precoder or equalizer designs in space-time coded MIMO channels.

The above-described deficiencies of wireless network communications are merely intended to provide an overview of some of the problems of today's wireless networks, and are not intended to be exhaustive. Other problems with the state of the art may become further apparent upon review of the description of various non-limiting embodiments that follows.

SUMMARY OF THE INVENTION

The following presents a simplified summary of the invention in order to provide a basic understanding of some aspects of the invention. This summary is not an extensive overview of the invention. It is intended to neither identify key or critical elements of the invention nor delineate the scope of the invention. Its sole purpose is to present some concepts of the invention in a simplified form as a prelude to the more detailed description that is presented later.

A joint set of linear precoder designs is provided for single cell uplink multiuser space-time block coded multiple-input multiple-output (MIMO) systems with multi-packet reception (MPR) by exploiting outdated channel state information (CSI). By deriving the pairwise error probability (PEP) with respect to both minimum and average codeword distance design metrics, an optimization problem is derived subject to transmit power constraint for each user and dependent on the outdated channel state information. Due to the non-convex nature of the optimization problem, an iterative technique based on alternating minimization and projected gradient algorithm can be used to solve for a linear preceding structure for general space-time block code (STBC). The linear preceding structure is then sent from the base station (BS) to various consumer premise equipments (CPEs).

For orthogonal space-time block code (OSTBC), a simplified distributed technique is provided to solve for a closed-form solution. According to one aspect of the simplified distributed technique, the linear preceding matrices are instead derived at each of the CPEs. Thus, resources are saved and are available for other purposes.

To the accomplishment of the foregoing and related ends, certain illustrative aspects of the invention are described herein in connection with the following description and the annexed drawings. These aspects are indicative, however, of but a few of the various ways in which the principles of the invention may be employed and the present invention is intended to include all such aspects and their equivalents. Other advantages and novel features of the invention may become apparent from the following detailed description of the invention when considered in conjunction with the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram of an exemplary wireless communication network in which the aspects can be implemented.

FIG. 2 is a diagram of transmissions in a wireless communication network according to one aspect.

FIG. 3 is a graph of average codeword error probability versus iteration index.

FIG. 4 is a graph of average codeword error probability versus signal to noise ratio (SNR) for various precoder designs.

FIG. 5 is a graph of average codeword error probability versus the number of consumer premise equipments communicating with the base station for various precoder designs.

FIG. 6 is a graph of average codeword error probability for various precoder designs versus the number of receive antennas at a base station.

FIG. 7 is a graph of average codeword error probability versus SNR for various precoder designs using binary phase-shift keying.

FIG. 8 is a graph of average codeword error probability versus SNR for various precoder designs using 16QAM.

FIG. 9 is a graph of average codeword error probability versus correlation coefficient for various precoder designs.

FIG. 10 is a block diagram of a base station in accordance with an aspect of the present invention.

FIG. 11 is a block diagram of a consumer premise equipment in accordance with an aspect.

FIG. 12 is a block diagram of a consumer premise equipment in accordance with another aspect.

FIG. 13 is a flowchart of a method of a base station in accordance with an aspect of the present invention.

FIG. 14 is a flowchart of a method of a consumer premise equipment (CPE) in accordance with an aspect of the present invention.

FIG. 15 is a flowchart of a method of a consumer premise equipment in accordance with another aspect of the present invention.

FIG. 16 is a block diagram representing an exemplary non-limiting computing system or operating environment in which the present invention may be implemented.

DETAILED DESCRIPTION OF THE INVENTION

The present invention is now described with reference to the drawings, wherein like reference numerals are used to refer to like elements throughout. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present invention. It may be evident, however, that the present invention may be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to facilitate describing the present invention.

The combination of STBC and channel precoding can make a wireless communication system robust against channel fading while achieving both coding and diversity gains. A joint set of linear precoder design for uplink multiuser space-time block coded system, as well as a distributed precoder design for orthogonal STBC, is provided by exploiting outdated CSI.

The linear precoder design is determined as a solution to an optimization problem for minimizing pairwise error probability (PEP) subject to a transmit power constraint for each user (e.g, each consumer premise equipment (CPE)). Since that optimization problem turns out to be non-convex over all precoder matrices, an iterative technique is provided to solve the optimization problem for general STBC. The disclosed technique works for both minimum and average codeword distance design metrics.

In light of an attractive property of average distance design criterion, a simplified technique is provided for a closed-form linear preceding structure for orthogonal STBC (OSTBC). When the quality of the outdated CSI is very good, the closed-form linear precoder approaches a single-eigenmode beamformer that allocates all transmit power to the strongest eigenmode. On the other hand, a precoder with poor channel quality tends to perform beamforming on all eigenmodes with equal power allocation.

In accordance with one aspect, an exemplary wireless network environment 100 is a single cell uplink multiuser space-time block coded system where there are L number of consumer premise equipments (CPEs) communicating synchronously with a base station (BS) in a MIMO-STBC system with multi-packet reception (MPR). One skilled in the art will appreciate that, despite the name, CPEs can be mobile devices.

As illustrated in FIG. 1, all CPEs 110 are scheduled by the BS 105 to transmit packets in the same time slot t₂. When users are scheduled by the base station for packet transmission, perfect symbol level synchronization can occur among all users. However, it is worth mentioning that the above-mentioned synchronization can be achieved in practice by using dedicated timing and access intervals. For example, symbol level synchronization for uplink OFDMA in WiMAX is achieved by uplink ranging in IEEE 802.16e.

Although not shown, the BS is typically connected to another network, such as the Internet, public telephone network, or private networks and communicates data received from the CPEs to this network and also transmits data from the network back to the CPEs.

In one aspect, preceding matrices {F_(i) ε C^(M) ^(Ti) ^(×M) ^(Ti) }_(i=1) ^(L) are designed at the BS and sent to the respective CPEs through a dedicated feedback link at t₁ while the CPEs start packet transmission at t₂. At the end of the slot with some predictable delays, say t₂+τ₂, the BS 105 gathers all of the L packets for channel estimation, data detection, and precoder design for CPEs. For the sake of clarity, without loss of generality, all packets are assumed to have the same length and each time slot equals one packet duration.

Referring to FIG. 2, the BS 220 has M_(R) receive antennas 208 and the i-th CPE 230 has M_(T) _(i) transmit antennas 206, where i=1 , . . . , L. The BS 220 uses a joint maximum likelihood decoder 210 to decode received data. The block fading channel from the i-th CPE to the BS is modeled as a M_(R)×M_(T) _(i) matrix H_(i) that follows the complex Gaussian distribution with mean M_(H) _(i) ε C^(M) ^(R) ^(×M) ^(Ti) and covariance Σ_(H) _(i) _(H) _(i) ε C^(M) ^(Ti) ^(×M) ^(Ti) .

Each MIMO channel is assumed to be correlated at CPEs but fully uncorrelated at the receiver side. In particular, since rich scattering at a CPE exists which results in a larger angle spread, the antenna of a CPE needs to be separated by, say λ/2, for uncorrelated fading. Yet, due to space limitations and RF coupling in small embedded devices (e.g., the CPE), it is difficult to maintain completely uncorrelated antennas at the CPE. However, although the angular spread at the base station is smaller (e.g., ˜30 degrees for urban environment), the base station can afford to separate antennas by larger distance (e.g., 10λ or more) and hence, it is easier to achieve uncorrelated fading at the base station side.

Thus, it is better performance-wise to have further separated antennas at the base station to create more degrees of freedom in the MIMO channels. The degrees of freedom can be used for diversity, especially when the CSI at the transmitter (CSIT) is poor, or spatial multiplexing. In any case, performance will be improved with further separated antenna at the base station.

Let C_(i) be a M_(T) _(i) ×T codeword generated from the i-th space-time encoder 202, which maps the i-th CPE's transmitted signal vector S_(i) ε C^(T×1) into C_(i). The codeword is processed by a linear precoder F_(i) 204. Such a system setup not only helps adopt the codeword to various kinds of channel conditions, but also inherits all implementation advantages of non-precoded space-time block coded systems since both the STBC and the detection algorithm remain unchanged. Denote N ε C^(M) ^(R) ^(×T) as additive white Gaussian noise whose entries are zero mean complex Gaussian with variance σ_(n) ². The received symbol matrix R ε C^(M) ^(R) ^(×T) is given by

$\begin{matrix} {R = {{\sum\limits_{i = 1}^{L}{H_{i}F_{i}C_{i}}} + {N.}}} & \left( {{Equation}\mspace{20mu} 1} \right) \end{matrix}$

Since channel estimates can be obtained with much higher accuracy at the receiver, perfect CSI at the receiver (CSIR) is assumed for simplicity. However, in other embodiments, channel estimation error can be included during the course of precoder design.

Denote HΔ[H₁, . . . , H_(L)], FΔdiag{F₁, . . . , F_(L)} and CΔ[C₁ ^(T), . . . , C_(L) ^(T)]^(T), where (•)^(T) is the matrix transpose. A joint maximum-likelihood (ML) decoder is employed to compute the decision metric and decide in favor of the codeword with the minimum metric.

{Ĉ ₁ , . . . , Ĉ _(L) }=arg _(c) ₁ _(εc,∀) _(i) min∥R−HFC∥ _(F) ²   (Equation 2)

where Ĉ_(i) is the estimate of C_(i), C is the STBC codebook and ∥•∥_(F) represents the Frobenius norm.

Although perfect CSIR is available, outdated CSI can be exploited in the precoder design due to feedback delay. Consider a scenario where all precoding matrices {F_(i)}_(i=1) ^(L) are jointly designed at the BS based on the CSI at time slot t₁, namely H(t₁), and the CPEs are scheduled for packet transmission at t₂>t₁. Because of the feedback delay, H(t₂) is generally different from H(t₁), which implies that when the CPEs transmit the packets, the precoders designed at the BS have been outdated. In the following, an approach is provided in which the outdated CSI from the i-th CPE to the BS is represented by a M_(R)×M_(T) _(i) matrix Ĥ_(i) with mean M_(Ĥ) _(i) and covariance Σ_(Ĥ) _(i) _(Ĥ) _(i) that are perfectly known at the BS. Define ĤΔ[Ĥ₁, . . . , Ĥ_(L)], and M_(H|Ĥ) as the channel mean matrix of instantaneous channel H conditioned on the outdated CSI Ĥ_(i).

Based on the assumption that H_(i) and Ĥ_(i) are jointly Gaussian for all i, Σ_(HH|Ĥ) Δdiag{Σ_(H) ₁ _(H) ₁ _(|Ĥ) ₁ , . . . , Σ_(H) _(L) _(H) _(L) _(|Ĥ) _(L) }, which is the covariance matrix of H conditioned on Ĥ_(i), can be used for measuring channel uncertainly. In particular, when Σ_(HH|Ĥ)→0, the quality of the outdated CSI approaches the perfect channel state information, and the precoders designed at the BS are closely matched with the channels during which the CPEs start packet transmission.

The problem of minimizing the pairwise error probability however is subject to a transmit power constraint for each CPE. In particular, two design criteria are indicated for selecting an appropriate codeword distance product metric for objective functions and for formulating a linear precoder design as an optimization problem.

The PEP is defined as an error probability of choosing in favor of the nearest codeword C=[ C ₁ ^(T), . . . , C _(L) ^(T)]^(T) instead of the actual transmitted codeword C. The PEP between C and C is mathematically written as

Pr(C→ C|H,Ĥ)= Pr(∥R−HFC∥ _(F) ² >∥R−HF C∥ _(F) ² |C,H,Ĥ).   (Equation 3)

Averaging Equation 3 over ergodic realizations of H and applying the Chernoff bound yields:

$\begin{matrix} {{\Pr \left( \left. C\rightarrow\overset{\_}{C} \right. \middle| \hat{H} \right)} \leq {E_{H}{\left\{ {\exp \left\lbrack {{- \frac{1}{4\sigma_{n}^{2}}}{tr}\left\{ {{{HFA}\left( {C,\overset{\_}{C}} \right)}F^{*}H^{*}} \right\}} \right\rbrack} \right\}.}}} & \left( {{Equation}\mspace{14mu} 4} \right) \end{matrix}$

where (•)* is the complex conjugate transpose operator and A(C,Ĉ) is the codeword distance product metric.

In the succeeding function, the Chernoff bound in Equation 4 is considered as an objective function of the linear precoder according to one aspect.

Objective Function 1. For every realization of the conditional channel mean matrix M_(H|Ĥ) and the conditional covariance matrix Σ_(HH|Ĥ), a joint set of linear preceding matrices {F_(i)}_(i=1) ^(L) is determined such that the Chernoff bound in Equation 5 below can be minimized,

$\begin{matrix} {\left\{ F_{i} \right\}_{i = 1}^{L} \leq {\arg \; {\min\limits_{{\{ F_{i}\}}_{i = 1}^{L}}{E_{H}{\left\{ {\exp \left\lbrack {{- \frac{1}{4\sigma_{n}^{2}}}{tr}\left\{ {{{HFA}\left( {C,\overset{\_}{C}} \right)}F^{*}H^{*}} \right\}} \right\rbrack} \right\}.}}}}} & \left( {{Equation}\mspace{14mu} 5} \right) \end{matrix}$

The conditional PEP in Objective Function 1 can be highly dependent on both the transmitted codeword C and the mis-decoded codeword Ĉ through the codeword distance product metric A(C,Ĉ). Two distance metrics are considered, namely minimum distance design and average distance design.

The largest PEP conditioned on Ĥ is the dominant term at a high signal-to-noise ratio (SNR). Thus, a search is made for the codeword pairs C and Ĉ that gives the smallest value of A(C,Ĉ)

$\begin{matrix} {{A_{\min}\left( {C,\overset{\_}{C}} \right)} = {\arg \; {\min\limits_{C,{\overset{\_}{C} \in C}}{\left( {C - \overset{\_}{C}} \right){\left( {C - \overset{\_}{C}} \right)^{*}.}}}}} & \left( {{Equation}\mspace{20mu} 6} \right) \end{matrix}$

For general STBC, the number of combinations is exponentially proportional to the total number of CPEs. However when OSTBC is considered, Equation 6 can be simplified as A_(min)(C,Ĉ)=(d_(m)/P_(S))I_(M) _(T) _(T) where d_(m) is the minimum distance in the signal constellation and P_(S) is the average symbol power. In this case, the complexity is reduced and it is only linearly proportional to L.

Substituting A_(min)(C,Ĉ) into Equation 5 yields the Chernoff bound of the conditional maximum PEP. This upper bound can be utilized, in at least some embodiments, to derive a joint set of linear precoders so as to minimize precoding gain.

In another embodiment, an average preceding gain is used to derive a joint set of linear precoders. In particular, average preceding gain is based on the covariance of the codeword error resulting from averaging the products of all possible codeword pairs,

$\begin{matrix} {{A_{ave}\left( {C,\overset{\_}{C}} \right)} = {\frac{1}{T}{{E\left\lbrack {\left( {C - \overset{\_}{C}} \right)\left( {C - \overset{\_}{C}} \right)^{*}} \right\rbrack}.}}} & \left( {{Equation}\mspace{20mu} 7} \right) \end{matrix}$

Consider a scenario where the codewords of different CPEs are independent with one another, i.e., E{(C_(i)−Ĉ_(i))(C_(j)− C _(j))*}=0 for i≠j, the codeword distance product metric can be simplified as the following block diagonal matrix

$\begin{matrix} {{A_{ave}\left( {C,\overset{\_}{C}} \right)} = {\frac{1}{T}\begin{bmatrix} {A_{1}\left( {C_{1},\overset{\_}{C}} \right)} & \ldots & 0 \\ \vdots & ⋰ & \vdots \\ 0 & \ldots & {A_{L}\left( {C_{L},{\overset{\_}{C}}_{L}} \right)} \end{bmatrix}}} & \left( {{Equation}\mspace{20mu} 8} \right) \end{matrix}$

where A_(i)(C_(i), C _(i))ΔE{(C_(i)−Ĉ_(i))(C_(i)−Ĉ_(i))*}. Due to the fact that each of the i-th space-time precoders only acts on one column of the codeword at a time, and detection is performed jointly over the whole code-block of symbol periods, the equation above can be simplified as

${A_{i}\left( {C_{i},{\overset{\_}{C}}_{i}} \right)}\overset{\Delta}{=}{{E\left\{ {\left( {C_{i} - {\overset{\_}{C}}_{i}} \right)\left( {C_{i} - {\overset{\_}{C}}_{i}} \right)^{*}} \right\}} = {\frac{1}{T}{\sum\limits_{m \neq n}{p_{i,m,n}\Delta_{i,m,n}\Delta_{i,m,n}^{*}}}}}$

where Δ_(i,m,n)=C_(i,m)−C_(i,n) and p_(i,m,n) is the probability of the pair C_(i,m) and C_(i,n) among all pairs of distinct codewords of the i-th space-time encoder. Therefore, A_(ave)(C, C) is practically obtained as long as p_(i,m,n) is known for all pairs of the distinct codewords.

Although this embodiment does not provide the minimum precoding gain of the other embodiment, A(C,Ĉ) is a tractable block diagonal matrix in this embodiment, which allows the derivation of a closed-form linear precoding structure.

After an appropriate codeword distance product metric A(C,Ĉ) has been chosen, the joint linear precoder is formulated as an optimization problem. Based on the assumption that both H and Ĥ are jointly Gaussian, the probability density function of H given Ĥ follows the complex Gaussian distribution

$\begin{matrix} {{f_{H|\hat{H}}\left( H \middle| \hat{H} \right)} = \frac{\exp\left\lbrack {{- {tr}}\left\{ {\left( {H - M_{H|\hat{H}}} \right)^{*}{\sum\limits_{H|\hat{H}}^{- 1}\left( {H - M_{H|\hat{H}}} \right)}} \right\}} \right\rbrack}{\pi^{M_{T}M_{R}}\; {\det\left( \sum_{{HH}|\hat{H}} \right)}^{M_{R}}}} & \left( {{Equation}\mspace{14mu} 9} \right) \end{matrix}$

where M_(T)=Σ_(i=1) ^(L)M_(T) _(i) is the sum of transmit antennas of all CPEs. By substituting Equation 9 into Equation 5, the conditional upper-bounded PEP is expressed as

$\begin{matrix} {{{P\left( \left. C\rightarrow\overset{\_}{C} \right. \middle| \hat{H} \right)} \leq {\frac{{\det\left( \sum\limits_{{HH}|\hat{H}} \right)}^{M_{R}}}{{\det \left( {W\left( {F,C,\overset{\_}{C}} \right)} \right)}^{M_{R}}}{\exp\left\lbrack {{tr}\left\{ {{M_{H|\hat{H}}\left( {{W\left( {F,C,\overset{\_}{C}} \right)^{- 1}} - \sum\limits_{{HH}|\hat{H}}^{- 1}} \right)}\left( M^{*} \right)_{H|\hat{H}}} \right\}} \right\rbrack}}}\mspace{20mu} {where}} & \left( {{Equation}\mspace{20mu} 10} \right) \\ {\mspace{79mu} {{W\left( {F,C,\overset{\_}{C}} \right)} = {\frac{1}{4\sigma_{n}^{2}}{\sum\limits_{{HH}|\hat{H}}{{{FA}\left( {C,\overset{\_}{C}} \right)}F^{*}{\sum\limits_{{HH}|\hat{H}}{+ {\sum\limits_{{HH}|\hat{H}}.}}}}}}}} & \left( {{Equation}\mspace{20mu} 11} \right) \end{matrix}$

Note that when there is only one CPE, Equation 10 is equivalent to the upper-bounded PEP of a single-user MIMO-STBC system. Thus, Equation 10 is a generalization to multiple users. By taking the logarithm on both sides of Equation 10 and neglecting those terms that are independent of F, Objective Function 1 can be simplified as follows.

Objective Function 2. For every realization of M_(H|Ĥ) and Σ_(HH|Ĥ), a set of linear preceding matrices {F_(i)}_(i=1) ^(L) is designed such that the objective function in Equation 12 is minimized

$\begin{matrix} \begin{matrix} {\left\{ F_{i} \right\}_{i = 1}^{L} = {\arg \; {\min\limits_{{\{ F_{i}\}}_{i = 1}^{L}}{l\left( {F,C,\overset{\_}{C}} \right)}}}} \\ {= {\arg \; {\min\limits_{{\{ F_{i}\}}_{i = 1}^{L}}\left\{ {{{tr}\left\{ {M_{H|\hat{H}}{W\left( {F,C,\overset{\_}{C}} \right)}^{- 1}M_{H|\hat{H}}^{*}} \right\}} -} \right.}}} \\ {\left. {M_{R}\log \; \det \; \left( {W\left( {F,C,\overset{\_}{C}} \right)} \right)} \right\}.} \end{matrix} & \left( {{Equation}\mspace{20mu} 12} \right) \end{matrix}$

The first term in Equation 12 depends on the actual realization of the channel estimate via M_(H|Ĥ) while the second term depends only on the codeword pairs via W(F,C,Ĉ). Nevertheless, the joint linear precoder design can be mathematically modeled as the following optimization problem.

Optimization Problem 1. For every realization of M_(H|Ĥ) and Σ_(HH|Ĥ), a set of linear precoders {F_(i)}_(i=1) ^(L) is chosen such that the objective function in Equation 13 is minimized subject to the transmit power constraint for each CPE as expressed in Equation 14 as follows.

$\begin{matrix} {{\min\limits_{{\{ F_{i}\}}_{i = 1}^{L}}{\text{:}\mspace{11mu} {l\left( {F,C,\overset{\_}{C}} \right)}}} = {{{tr}\left\{ {M_{H|\hat{H}}{W\left( {F,C,\overset{\_}{C}} \right)}^{- 1}M_{H|\hat{H}}^{*}} \right\}} - {M_{R}\log \; \det \; \left( {W\left( {F,C,\overset{\_}{C}} \right)} \right)}}} & \left( {{Equation}\mspace{20mu} 13} \right) \\ {\mspace{79mu} {{{{subject}\mspace{14mu} {to}\text{:}\mspace{11mu} {tr}\left\{ {F_{i}F_{i}^{*}} \right\}} = P_{i}},{i = 1},\ldots \mspace{11mu},L}} & \left( {{Equation}\mspace{20mu} 14} \right) \end{matrix}$

with P_(i) being the transmit power for the i-th CPE.

Due to the nonlinear nature of FA(C,Ĉ)F* in W(F,C,Ĉ), Optimization Problem 1 is not jointly convex over F and therefore, certain algorithms such as interior point methods cannot be used for solving the problem directly. Thus, in one embodiment, an iterative technique is employed to solve for the joint set of linear precoding matrices. The resulting precoding matrices are suitable for any STBC implementation.

According to the iterative technique, alternating minimization is employed such that a set of variables are optimized one at a time while keeping all the other variables fixed. For a joint formulation of linear precoders, alternating minimization starts with a given set of precoding structures, and then is updated using the following Optimization Problem in a parallel fashion

Optimization Problem 2. Given the preceding matrices F_(j) of those L−1 CPEs, where j≠i, a linear precoder F_(i) is chosen for every realization of M_(H|Ĥ) and Σ_(HH|Ĥ) such that the objective function in Equation 15 is minimized subject to the transmit power constraint as follows:

$\begin{matrix} {{\min\limits_{F_{i}}{\text{:}\mspace{11mu} {l\left( {F,C,\overset{\_}{C}} \right)}}} = {{{tr}\left\{ {M_{H|\hat{H}}{W\left( {F,C,\overset{\_}{C}} \right)}^{- 1}M_{H|\hat{H}}^{*}} \right\}} - {M_{R}\log \; \det \; \left( {W\left( {F,C,\overset{\_}{C}} \right)} \right)}}} & \left( {{Equation}\mspace{20mu} 15} \right) \\ {\mspace{79mu} {{{subject}\mspace{14mu} {to}\text{:}\mspace{11mu} {tr}\left\{ {F_{i}F_{i}^{*}} \right\}} = {P_{i}.}}} & \left( {{Equation}\mspace{20mu} 16} \right) \end{matrix}$

For the above optimization problem, a modified version of a standard unconstrained gradient algorithm is used as a projected gradient algorithm to take into account the transmit power constraint for each CPE.

Denote F_(i) ^((l)) as the i-th linear precoder at iteration l and F^((l)) Δdiag{F₁ ^((l)), . . . , F_(L) ^((l))}. Further denote d as the step size that is chosen for guaranteeing the convergence of the algorithm. An iteration is defined as

$\begin{matrix} {F_{i}^{({l + 1})} = \left\lbrack {F_{i}^{(l)} - {\frac{\sqrt{P_{i}}}{d}\frac{\nabla_{F_{i}^{(l)}}^{*}{l\left( {F^{(l)},C,\overset{\_}{C}} \right)}}{\sqrt{{{\nabla_{F_{i}^{(l)}}{l\left( {F^{(l)},C,\overset{\_}{C}} \right)}}}_{F}^{2}}}}} \right\rbrack_{\bot}} & \left( {{Equation}\mspace{20mu} 17} \right) \end{matrix}$

where ∇ represents the matrix-valued nabla operator and [•]_(⊥) is the projection onto the transmit power constraint for the i-th CPE over F_(i) ^((i+1)).

The gradient, ∇_(F) _(i) _((l)) l(F^((l)),C,Ĉ), is computed using the Karush-Kuhn-Tucker (KKT) optimality conditions of Optimization Problem 2, and subsequently the derived gradient is projected onto the transmit power constraint set. In particular, by introducing the Lagrangian multiplier constant μ_(i)≧0 for the i-th CPE and taking the first order differentiation with respect to F_(i) ^((l)) for Equations 15 and 16, it can be shown that

                                     (Equation  18) ${\mu_{i}F_{i}^{(l)}} = {{\nabla_{F_{i}}\left( {F^{(l)},C,\overset{\_}{C}} \right)} = {\frac{1}{2\sigma_{n}^{2}}E_{i}{\Sigma_{{HH}|\hat{H}}\left( {{{W\left( {F^{(l)},C,\overset{\_}{C}} \right)}^{- 1}M_{H|\hat{H}}^{*}M_{H|\hat{H}}{W\left( {F^{(l)},C,\overset{\_}{C}} \right)}^{- 1}} + {M_{R}{W\left( {F^{(l)},C,\overset{\_}{C}} \right)}^{- 1}}} \right)} \times \Sigma_{{HH}|\hat{H}}F^{(l)}{A\left( {C,\overset{\_}{C}} \right)}E_{i}^{T}}}$

where E_(i)=[0, . . . , I_(M) _(T1) , . . . , 0] is the basis matrix of size M_(T) ₁ ×M_(T)L.

Thus, an exemplary iterative technique for solving the optimization problem according to one aspect for the joint set of linear precoder design is outlined in Table 1. While gradient descent is utilized, one skilled in the art will appreciate the optimization problem can be solved using other optimization algorithms in other embodiments. In general, the exemplary technique is mainly divided into two distinct parts, namely the initialization and the iteration parts.

TABLE I Iterative Algorithm for the Joint Linear Precoder Design Initialization $\begin{matrix} {{d = 2};{l = 0};} \\ {{{for}\mspace{14mu} i} = {1:L}} \\ {F_{i}^{(0)} = {\sqrt{\frac{P_{i}}{M_{T_{l}}}}I_{M_{T_{l}}}}} \\ {end} \end{matrix}\quad$ Iteration l = l + 1; for i = 1 : L $\begin{matrix} {{{\delta F}_{i}^{(l)} = {\nabla_{F_{l}^{({l - 1})}}^{*}{l\left( {F^{({l - 1})},C,\overset{\_}{C}} \right)}}};} \\ {{{\delta F}_{i}^{(l)} = {\sqrt{\frac{P_{l}}{{{\delta F}_{l}^{(l)}}_{F}^{2}}}{\delta F}_{i}^{(l)}}};} \end{matrix}\quad$ $\begin{matrix} {{F_{i}^{(l)} = {{\frac{1}{d}{\delta F}_{i}^{(l)}} + {\delta F}_{i}^{({l - 1})}}};} \\ {{F_{i}^{(l)} = {\sqrt{\frac{P_{l}}{{F_{l}^{(l)}}_{F}^{2}}}F_{i}^{(l)}}};} \\ {end} \\ {if} \\ {{l\left( {F^{(l)},C,\overset{\_}{C}} \right)} > {l\left( {F^{({l - 1})},C,\overset{\_}{C}} \right)}} \\ {{d = {d + 1}};{l = {l - 1}};} \\ {end} \end{matrix}\quad$ Termination

For the first part, the iteration index l=0 and the step size is initialized as d=2. In addition, all of the L preceding matrices are initialized as F_(i) ⁽⁰⁾=√{square root over (P_(i)/M_(T) ₁ )}I_(M) _(T1) , for i=1, L such that the transmit power constraint for each user can be satisfied.

The second part computes the standard gradient and performs projection to satisfy the transmit power constraint. In particular, the normalized gradient ∇_(F) _(i) _((l−1)) l(F^((l−1)),C,Ĉ) is computed, the precoding matrix F_(i) ^((l−1)) is updated to F_(i) ^((l)), while a projection onto the transmit power constraint for the i-th CPE is done. A subsequent comparison is performed to check if the objective function acquired with the updated precoding matrices is larger than that of the previous one. If so, the step size should be adjusted and all of the L preceding matrices from the previous iteration l−1 should be re-calculated. Finally, the iterative algorithm is terminated when |l(F^((l)),C,Ĉ)−l(F^((l−1)),C,Ĉ)|<ε, where ε is a small constant.

The convergence of the algorithm is guaranteed and proven by means of a descent argument. Suppose the objective function ƒ is bounded below and Lipschitzian with the Lipschitz constant L, and 0<1/d<2/L, where d is the step size. The sequence generated by the projected gradient algorithm then converges. Furthermore, the limit point of this sequence satisfies the first order KKT optimality condition. In other words, the only requirement for the convergence is to choose a suitable value of d such that its value is smaller than the Lipschitz constant. Referring to FIG. 3, FIG. 3 shows the convergence rate for the 2-user MIMO-STBC system when ρ=0.5, 0.9, SNR=6 dB, and M_(R)=2. In particular, the curves 305 and 310 for ρ=0.5, 0.9, respectfully, illustrate that the technique converges after running about 20 iterations.

Although the joint set of linear precoders can be solved numerically through the iterative algorithm, there is a special case in which a closed-form precoding structure exists, which allows the behavior of linear precoders to be investigated when the channel quality is at two extremes (very poor and very good) and derive a closed-form expression on the Chernoff bound of the conditional PEP at high SNR.

One attractive property of the average distance design embodiment is that when OSTBC is applied, the codeword distance product metric can be significantly simplified as a scaled identity matrix, namely A_(ave,OSTBC)(α)=αI_(M) _(T) _(L), where α is the average distance determined by both the constellation of the system and the code itself. Thus, by considering a precoder design with OSTBC subject to the average distance design criterion, the dependence of the conditional upper-bounded PEP on the codeword pairs is now solely through the codeword-dependent parameter α. Interestingly, if the conditional covariance matrix is also a scaled identity matrix, the centralized precoder design can be decoupled in an embodiment into a distributed design in which each of the L linear preceding matrices is derived at the respective CPE.

If each entry of H and Ĥ is a complex Gaussian random variable with the same variance, say σ_(h) ², the covariance matrix of H conditioned on Ĥ is given by

Σ_(HH|Ĥ=σ) _(h) ²(1−ρ²)I _(M) _(T) _(L)   (Equation 19)

where ρ=E{H(i,j)Ĥ(i,j)}/σ_(h) ² is the normalized correlation coefficient between the (i,j)-th entries of H and Ĥ, which are denoted by H(i,j) and Ĥ(i,j), respectively.

Based on the block diagonal structures of A_(ave,OSTBC)(α) and Σ_(HH|Ĥ), the metric W(F,C,Ĉ) given in Equation 11 is greatly simplified as

                                (Equation  20) ${W_{{ave},{OSTBC}}\left( {F,\alpha} \right)} = {{\sigma_{h}^{2}\left( {1 - \rho^{2}} \right)}\left( {{\frac{\alpha}{4\sigma_{n}^{2}}{\sigma_{h}^{2}\left( {1 - \rho^{2}} \right)}{FF}^{*}} + I_{M_{T}L}} \right)}$                                 (Equation  21) $\begin{matrix} {{\overset{\Delta}{=}\begin{bmatrix} {W_{1}\left( {F_{1},\alpha} \right)} & \ldots & 0 \\ \vdots & ⋰ & \vdots \\ 0 & \ldots & {W_{L}\left( {F_{L},\alpha} \right)} \end{bmatrix}}\mspace{230mu} \; {where}} \\ {{W_{i}\left( {F_{i},\alpha} \right)}\overset{\Delta}{=}{{\sigma_{h}^{2}\left( {1 - \rho^{2}} \right)}{\left( {{\frac{\alpha}{4\sigma_{n}^{2}}{\sigma_{h}^{2}\left( {1 - \rho^{2}} \right)}F_{i}F_{i}^{*}} + I_{M_{T_{i}}}} \right).}}} \end{matrix}$

By substituting Equation 20 into Equation 12, the objective function l(F,C,Ĉ) is decomposed into L terms as

$\begin{matrix} {{l_{{ave},{OSTBC}}\left( {F,\alpha} \right)} = {\sum\limits_{i = 1}^{L}\left\{ {{{tr}\left\{ {M_{H_{i}|{\hat{H}}_{i}}{W_{i}\left( {F_{i},\alpha} \right)}^{- 1}M_{H_{i}|{\hat{H}}_{i}}^{*}} \right\}} - {M_{R}\log \; {\det \left( {W_{i}\left( {F_{i},\alpha} \right)} \right)}}} \right\}}} & \left( {{Equation}\mspace{14mu} 22} \right) \\ {\mspace{79mu} {\overset{\Delta}{=}{\sum\limits_{i = 1}^{L}{{l_{i}\left( {F_{i},\alpha} \right)}.}}}} & \left( {{Equation}\mspace{14mu} 23} \right) \end{matrix}$

Thus, the multiuser linear precoder design can be decoupled into L single-user precoder designs with the optimization problem is posed as follows.

Optimization Problem 3. For every realization of M_(H) ₁ _(|Ĥ) ₁ and ΣH ₁ _(H) ₁ _(|Ĥ) ₁ , a linear precoder F_(i) is chosen such that the PEP-related objective function l_(i)(F,α) is minimized subject to a transmit power constraint as expressed in Equation 25 as follows.

$\begin{matrix} {{\min\limits_{F_{i}}{\text{:}\mspace{11mu} {tr}\left\{ {M_{H_{i}|{\hat{H}}_{i}}{W_{i}\left( {F_{i},\alpha} \right)}^{- 1}M_{H_{i}|{\hat{H}}_{i}}^{*}} \right\}}} - {M_{R}\log \; \text{det}\left( {W_{i}\left( {F_{i},\alpha} \right)} \right)}} & \left( {{Equation}\mspace{20mu} 24} \right) \\ {\mspace{79mu} {{{subject}\mspace{20mu} {to}\text{:}\mspace{11mu} {tr}\left\{ {F_{i}F_{i}^{*}} \right\}} = P_{i}}} & \left( {{Equation}\mspace{20mu} 25} \right) \end{matrix}$

with W_(i)(F_(i),α) being defined in Equation 21.

By using the KKT optimality conditions, F_(i) can be analytically solved. In particular, when average precoding gain is used to determine the linear precoder and orthogonal space-time block code (OSTBC) is applied, the linear precoder of the i-th CPE has the closed-form structure.

                                (Equation  26) $F_{i} = {{U_{M_{H_{i}|{\hat{H}}_{i}}}\Lambda_{F_{i}}{{where}\left( {\Lambda_{F_{i}}\left( {k,k} \right)} \right)}^{2}} = \left( \frac{\begin{matrix} {{\frac{2\sigma_{n}^{2}}{\mu_{i}}\left( {M_{R} + \sqrt{M_{R}^{2} + {4\mu_{i}{\Lambda_{M_{H_{i}|{\hat{H}}_{i}}}\left( {k,k} \right)}}}} \right)} -} \\ {4\sigma_{n}^{2}{\sigma_{h}^{2}\left( {1 - \rho^{2}} \right)}} \end{matrix}}{{{\alpha\sigma}_{h}^{4}\left( {1 - \rho^{2}} \right)}^{2}} \right)^{+}}$

with (•)⁺=max(0,x), μ_(i)≧0 being the Lagrangian multiplier associated with the transmit power constraint for the i-th CPE and M*_(H) ₁ _(|Ĥ) ₁ M_(H) ₁ _(|Ĥ) ₁ =U_(M) _(H1) _(|Ĥ1) Λ_(M) _(H1) _(|Ĥ1) U*_(M) _(H1) _(|Ĥ1) via eigenvalue decomposition.

In the closed-form preceding structure in Equation 26, the optimal left singular vector is the eigenvector of M*_(H) _(i) _(|Ĥ) _(i) M_(H) _(i) _(|Ĥ) _(i) , which implies that the beam directions of the i-th linear precoder depend on channel knowledge obtained from the actual realization of the channel estimate.

The behavior of linear precoders is investigated when the quality of the outdated CSI is at two extremes. In addition, asymptotic closed-form expression on the Chernoff bound of the conditional PEP is also derived.

With good channel quality, or equivalently ρ→1, the conditional covariance approaches zero, i.e., Σ_(HH|Ĥ)→0. Using the fact that the matrix inverse (I−Q)⁻¹=Σ_(q=0) ^(∞)Q^(q) exists when the matrix norm of Q is smaller than one, when ρ→1,

$\begin{matrix} {{W_{{ave},{OSTBC}}\left( {F,\alpha} \right)}^{- 1} = {\frac{1}{\sigma_{h}^{2}\left( {1 - \rho^{2}} \right)}{\sum\limits_{k = 0}^{\infty}{\left( {{- \frac{\alpha}{4\sigma_{n}^{2}}}{\sigma_{h}^{2}\left( {1 - \rho^{2}} \right)}{FF}^{*}} \right)^{k}.}}}} & \left( {{Equation}\mspace{20mu} 27} \right) \end{matrix}$

By neglecting the higher order terms of Equation 27 and after a series of manipulation, the conditional upper-bounded PEP in Equation 10 can be simplified as

$\begin{matrix} {{\Pr \left( \left. C\rightarrow\overset{\_}{C} \right. \middle| \hat{H} \right)} < {{\exp \left\lbrack {{- \frac{\alpha}{4\sigma_{n}^{2}}}{tr}\left\{ {\sum\limits_{i = 1}^{L}{M_{H_{i}|{\hat{H}}_{i}}F_{i}F_{i}^{*}M_{H_{i}|{\hat{H}}_{i}}^{*}}} \right\}} \right\rbrack}.}} & \left( {{Equation}\mspace{20mu} 28} \right) \end{matrix}$

Since minimizing Equation 28 is equivalent to maximizing tr{M_(H) _(i) _(|Ĥ) _(i) F_(i)F*_(i)M*_(H) _(i) _(|Ĥ) _(i) }, the optimization problem for linear precoder design can be rewritten as follows:

Optimization Problem 4. If the outdated CSI is assumed to be of excellent quality, for every realization of M_(H) _(i) _(|Ĥ) _(i) and Σ_(H) _(i) _(H) _(i) _(|Ĥ) _(i) , a linear precoder F_(i) is formulated such that the objective function in Equation 29 is maximized subject to the transmit power constraint as expressed in Equation 30 as follows.

$\begin{matrix} {\max\limits_{F_{i}}{\text{:}\mspace{11mu} {tr}\left\{ {M_{H_{i}|{\hat{H}}_{i}}F_{i}F_{i}^{*}M_{H_{i}|{\hat{H}}_{i}}^{*}} \right\}}} & \left( {{Equation}\mspace{20mu} 29} \right) \\ {{{subject}\mspace{14mu} {to}\text{:}\mspace{11mu} {tr}\left\{ {F_{i}F_{i}^{*}} \right\}} = {P_{i}.}} & \left( {{Equation}\mspace{20mu} 30} \right) \end{matrix}$

Similar to the previous optimization problem, the solution is obtained by using the KKT optimality conditions and the closed-form precoding structure is given by

F _(i) =U _(M) _(Hi) _(|Ĥi) {circumflex over (Λ)}_(F) _(i)   (Equation 31)

where {circumflex over (Λ)}_(F) _(i) =diag{√{square root over (P_(i))}, 0, . . . , 0}. By comparing the resulting preceding structures of Optimization Problems 3 and 4, the power allocation strategy changes significantly to allocate all transmit power on the strongest eigenmode. Thus, as the quality of the outdated CSI increases, the linear precoder of the i-th CPE drops eigenmodes until it becomes a single-eigenmode beamformer, which agrees with the asymptotic result for single-user scenario where perfect CSI is available at the transmitter.

By substituting Equation 31 into Equation 27, a simplified Chernoff bound in Equation 10 is determined:

                                     (Equation  32) ${{\Pr \left( \left. C\rightarrow\overset{\_}{C} \right. \middle| \hat{H} \right)} \leq {\prod\limits_{i = 1}^{L}{\frac{{\det \left( {{\sigma_{h}^{2}\left( {1 - \rho^{2}} \right)}I_{M_{T_{i}}}} \right)}^{M_{R}}}{{\det\left( {{\sigma_{h}^{2}\left( {1 - \rho^{2}} \right)}\left( {{\frac{\alpha}{4\sigma_{n}^{2}}{\sigma_{h}^{2}\left( {1 - \rho^{2}} \right)}{\overset{\_}{\Lambda}}_{F_{i}}} + I_{M_{T_{i}}}} \right)} \right)}^{M_{R}}} \times {\exp\left\lbrack {{tr} \left\{ {\frac{1}{\sigma_{h}^{2}\left( {1 - \rho^{2}} \right)} {\Lambda_{M_{H_{i}|{\hat{H}}_{i}}}\left( {\left( {{\frac{\alpha}{4\sigma_{n}^{2}}{\sigma_{h}^{2}\left( {1 - \rho^{2}} \right)}{\overset{\_}{\Lambda}}_{F_{i}}} + I_{M_{T_{i}}}} \right)^{- 1} - I_{M_{T_{i}}}} \right)}} \right\}} \right\rbrack}}}} = {\prod\limits_{i = 1}^{L}{\left( {{\frac{\alpha \; P_{i}}{4\sigma_{n}^{2}}{\sigma_{h}^{2}\left( {1 - \rho^{2}} \right)}} + 1} \right)^{- M_{R}}\; {{\exp\left\lbrack {\frac{\Lambda_{M_{H_{i}|{\hat{H}}_{i}}}\left( {1,1} \right)}{\sigma_{h}^{2}\left( {1 - \rho^{2}} \right)} \left( {\left( {{\frac{\alpha \; P_{i}}{4\sigma_{n}^{2}}{\sigma_{h}^{2}\left( {1 - \rho^{2}} \right)}} + 1} \right)^{- 1} - 1} \right)} \right\rbrack}.}}}$

Therefore, the conditional PEP decreases exponentially with the correlation coefficient ρ. In addition, the PEP improves with the codeword-dependent parameter α and the transmit power P_(i) at a polynomial rate of M_(R) while PEP degrades with the number of CPEs L.

When ρ→0 and Σ_(HH|Ĥ)→σ_(h) ²I_(M) _(T) _(L), the optimal linear precoder is a scaled identity matrix

$\begin{matrix} {F_{i} = {\sqrt{\frac{P_{i}}{M_{T_{i}}}}I_{M_{T_{i}}}}} & \left( {{Equation}\mspace{20mu} 33} \right) \end{matrix}$

which indicates that when the quality of the outdated CSI deteriorates, the precoder tends to have beamforming on all eigenmodes with equal power allocation. This solution also agrees with the asymptotic result for a single-user scenario where no CSI is available at the transmitter. By substituting Equation 33 into Equation 10, the conditional upper-bounded PEP is simplified as:

                                     (Equation  34) ${\Pr \left( \left. C\rightarrow\overset{\_}{C} \right. \middle| \hat{H} \right)} \leq {\prod\limits_{i = 1}^{L}{\left( {{\frac{\alpha \; P_{i}}{4M_{T_{i}}\sigma_{n}^{2}}{\sigma_{h}^{2}\left( {1 - \rho^{2}} \right)}} + 1} \right)^{{- M_{R}}M_{T_{i}}} \times {{\exp \left\lbrack {\frac{{tr}\left\{ {M_{H_{i}|{\hat{H}}_{i}}M_{H_{i}|{\hat{H}}_{i}}^{*}} \right\}}{\sigma_{h}^{2}\left( {1 - \rho^{2}} \right)}\left( {\left( {{\frac{\alpha \; P_{i}}{4M_{T_{i}}\sigma_{n}^{2}}{\sigma_{h}^{2}\left( {1 - \rho^{2}} \right)}} + 1} \right)^{- 1} - 1} \right)} \right\rbrack}.}}}$

Similar to Equation 32, the conditional PEP derived above decreases exponentially with ρ. In addition, it is not only a function of the codeword-dependent parameter, the numbers of receive antennas, and the number of CPEs, but also a function of transmit antennas. Although Equations 32 and 34 are derived using a high-SNR assumption, these Chernoff bounds can still be successfully utilized in situations where the SNR is not high.

The average codeword error probability of the multiuser MIMO-STBC system is investigated according to an aspect of the linear precoder technique. For all of the simulations, unless otherwise indicated, σ_(h) ²=1 is assumed and the inputs to the space-time encoders are assumed to form an independently and identically distributed sequence of equally-probable symbols.

The performance of the centralized and distributed precoder designs are compared for a multiuser MIMO system with Alamouti's STBC. All elements of the codewords are taken from a Binary Phase-Shift Keying (BPSK) constellation, in which α=2.13, unless otherwise indicated.

The performance of both aspects with different channel qualities, namely ρ=0, 0.5, 0.9 are demonstrated. Referring to FIG. 4, the performances of both average distance design and minimum distance design embodiments are exactly the same, which reveals the benefit of using the average distance design criterion for OSTBC. In particular, the linear precoder can now be designed at the respective CPE.

More importantly, when no outdated CSI is available, i.e., ρ=0, the performances of both designs reduce to that of the conventional beamforming system, which can be justified by the asymptotic analysis that when ρ→0, the precoder tends to perform beamforming on all eigenmodes with equal power allocation which results in no precoding gain. On the other hand, when the outdated CSI is of better quality, the codeword error probability of both designs outperforms that of the system with no precoder by 2-5 dB.

Referring to FIGS. 5 and 6, the effects of the number of CPEs L and the number of receive antennas M_(R) respectfully are illustrated. In FIG. 5, the system performance is at SNR=10 dB and ρ=0.9. Curves 505, 510, and 515 for no precoder, the centralized embodiment, and the disturbed embodiment are shown, respectively. Both precoder designs outperform the conventional system with no channel precoding by about one order of magnitude in terms of average codeword error probability. The effect of M_(R) is depicted in FIG. 6 at SNR=5 dB and ρ=0.9. Curves 605, 610, and 615 for no precoder, the centralized embodiment, and the disturbed embodiment are shown, respectively. In particular, as shown in FIG. 6, the system performance improves with the number of receive antennas due to the diversity gain. In addition, both precoder embodiments also outperform the conventional system by about one order of magnitude.

Non-orthogonal STBC, namely quasi-orthogonal space-time block code (QSTBC), is considered and compared to the performance of the centralized design with minimum and average codeword distance design criteria. FIGS. 7 and 8 illustrate the average codeword error probability for the system with BPSK and 16QAM modulations, respectively, at ρ=0.9. Curves 705/805, 710/810, and 715/815 represent a conventional system with no precoder, an average distance embodiment, and a minimum distance embodiment, respectively in FIGS. 7 and 8. When the constellation level is small, the performance of the centralized design with average distance design criterion is exactly the same as that with minimum distance one. However, the preceding gain of the average distance design decreases with the constellation level since the number of minimum-distance codeword pairs becomes larger and more dominant in affecting the system performance.

As the final example, the effect of channel quality is shown in FIG. 9. Curve 905 illustrates the conventional system without a precoder and curve 910 illustrates the performance of both precoder embodiments. The SNR is set at 5 dB and the modulation is BPSK. Thus, when the channel quality is poor (ρ→0), the performance is the same as that of the conventional beamforming. On the other hand, significant preceding gains are achieved as ρ→1.

FIGS. 10-12 illustrate components and/or hardware of a base station and a CPE according to aspects of the disclosed techniques. For the sake of clarity, well-known hardware structure (e.g., multiplexers, decoders) used in base stations and/or consumer premise equipments as well as other components providing other functionality are not shown and described. However, one skilled in the art will appreciate that these additional hardware and component can be present.

Referring to FIG. 10, FIG. 10 illustrates an exemplary base station according to one embodiment. The base station comprises multiple antennas 1002, which can include multiple receive and transmit antennas for uplink and downlink, respectively. The channel state information component 1004 receives an indication of outdated channel state information. This indication can be from a local component (not shown) estimating that locally or from one or more CPEs. The power constraint component 1006 receives an indication of the transmit power constraints from at least one CPE. The optimization component 1008 solves the optimization problem subject to the indicated power constraints and based on the outdated channel state information. For example, the optimization can use the iterative technique described supra. The transmission component 1010 transmits the determined linear precoders to each of the CPEs.

FIG. 11 illustrates an exemplary CPE in accordance with a centralized embodiment. A precoder receiving component 1102 receives a linear precoder to use from the base station and the transmitting component 1106 transmits data in accordance with the indicated linear precoder. Power constraint component 1104 indicates the transmit power constraint to the base station. Similarly, channel state information component 1108 can detect the current channel state information and relay the CSI to the base station.

FIG. 12 illustrates an exemplary CPE in accordance with OSTBC. The components/hardware are similar to those described with respect to FIG. 10 on the base station, except optimization component 1208 solves the simplified closed form optimization problem. In addition, instead of indicating the linear precoder to the CPEs, transmission component 1210 transmits data in accordance with the linear precoder determined as the solution to the optimization problem.

Turning briefly to FIGS. 13-15, methodologies that may be implemented in accordance with the present invention are illustrated. While, for purposes of simplicity of explanation, the methodologies are shown and described as a series of blocks, it is to be understood and appreciated that the present invention is not limited by the order of the blocks, as some blocks may, in accordance with the present invention, occur in different orders and/or concurrently with other blocks from that shown and described herein. Moreover, not all illustrated blocks may be required to implement the methodologies in accordance with the present invention. Furthermore, although for the sake of clarity, the methods are shown for a single determination of the linear precoder, one will appreciate that these methods can be preformed continuously.

FIG. 13 illustrates an exemplary method at a base station according to one embodiment. At 1305, an indication of outdated CSI is received. The indication can come from a component of the base station that estimates the CSI or can be received from one or more devices (e.g., CPEs) in the wireless network. At 1310, an indication of the transmit power constraints for each of the CPEs is received. For example, the CPEs can transmit this information or this information can be retrieved from a local cache of the transmit power constraints. At 1315, the optimization problem is solved subject to the indicated power constraints. As stated above, the solution can, for example, be performed using the iterative technique described above. However, one will appreciate that other techniques for solving optimization problems can also be utilized in other embodiments. At 1320, one or more linear precoders are determined from the solution to the optimization problem. For example, a linear precoder can be indicated to each of the CPEs on the wireless network.

Referring to FIG. 14, an exemplary method of a CPE according to a centralized design embodiment is illustrated. At 1405, the transmit power constraint and optionally channel state information is indicated to the base station. At 1410, an indication is received from the base station of a linear precoder. At 1415, data can be transmitted in accordance with the indicated linear precoder.

Referring to FIG. 15, an exemplary method of the distributed technique for OSTBC at a CPE is illustrated. At 1505, an indication of outdated CSI is received. The indication can come from a component of the CPE that estimates the CSI or can be received from one or more devices (e.g., other CPEs or the BS) in the wireless network. At 1510, an indication of the transmit power constraints for the CPE is received. At 1515, the closed form optimization problem is solved subject to the indicated power constraint. At 1520, data is transmitted using the linear precoders determined as a solution to the optimization problem.

Turning to FIG. 16, an exemplary non-limiting computing system or operating environment in which the present invention may be implemented is illustrated. One of ordinary skill in the art can appreciate that handheld, portable and other computing devices and computing objects of all kinds are contemplated for use in connection with the present invention, i.e., anywhere that a communications system may be desirably configured. Accordingly, the below general purpose remote computer described below in FIG. 16 is but one example of a computing system in which the present invention may be implemented.

Although not required, the invention can partly be implemented via an operating system, for use by a developer of services for a device or object, and/or included within application software that operates in connection with the component(s) of the invention. Software may be described in the general context of computer-executable instructions, such as program modules, being executed by one or more computers, such as client workstations, servers or other devices. Those skilled in the art will appreciate that the invention may be practiced with other computer system configurations and protocols.

FIG. 16 thus illustrates an example of a suitable computing system environment 1600 in which the invention may be implemented but the computing system environment 1600 is only one example of a suitable computing environment and is not intended to suggest any limitation as to the scope of use or functionality of the invention. Neither should the computing environment 1600 be interpreted as having any dependency or requirement relating to any one or combination of components illustrated in the exemplary operating environment 1600.

With reference to FIG. 16, an example of a computing device for implementing the invention includes a general purpose computing device in the form of a computer 1610. Components of computer 1610 may include, but are not limited to, a processing unit 1620, a system memory 1630, and a system bus 1621 that couples various system components including the system memory to the processing unit 1620. The system bus 1621 may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures.

Computer 1610 typically includes a variety of computer readable media. Computer readable media can be any available media that can be accessed by computer 1610. By way of example, and not limitation, computer readable media may comprise computer storage media and communication media. Computer storage media includes volatile and nonvolatile as well as removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CDROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by computer 1610. Communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media.

The system memory 1630 may include computer storage media in the form of volatile and/or nonvolatile memory such as read only memory (ROM) and/or random access memory (RAM). A basic input/output system (BIOS), containing the basic routines that help to transfer information between elements within computer 1610, such as during start-up, may be stored in memory 1630. Memory 1630 typically also contains data and/or program modules that are immediately accessible to and/or presently being operated on by processing unit 1620. By way of example, and not limitation, memory 1630 may also include an operating system, application programs, other program modules, and program data.

The computer 1610 may also include other removable/non-removable, volatile/nonvolatile computer storage media. For example, computer 1610 could include a hard disk drive that reads from or writes to non-removable, nonvolatile magnetic media, a magnetic disk drive that reads from or writes to a removable, nonvolatile magnetic disk, and/or an optical disk drive that reads from or writes to a removable, nonvolatile optical disk, such as a CD-ROM or other optical media. Other removable/non-removable, volatile/nonvolatile computer storage media that can be used in the exemplary operating environment include, but are not limited to, magnetic tape cassettes, flash memory cards, digital versatile disks, digital video tape, solid state RAM, solid state ROM and the like. A hard disk drive is typically connected to the system bus 1621 through a non-removable memory interface such as an interface, and a magnetic disk drive or optical disk drive is typically connected to the system bus 1621 by a removable memory interface, such as an interface.

A user may enter commands and information into the computer 1610 through input devices. Input devices are often connected to the processing unit 1620 through user input 1640 and associated interface(s) that are coupled to the system bus 1621, but may be connected by other interface and bus structures, such as a parallel port, game port or a universal serial bus (USB). A graphics subsystem may also be connected to the system bus 1621. A monitor or other type of display device is also connected to the system bus 1621 via an interface, such as output interface 1650, which may in turn communicate with video memory. In addition to a monitor, computers may also include other peripheral output devices, which may be connected through output interface 1650.

The computer 1610 operates in a networked or distributed environment using logical connections to one or more other remote computers, such as remote computer 1670, which may in turn have capabilities different from device 1610. The logical connections depicted in FIG. 16 include a network 1671. The network 1671 can include both the wireless network described herein as well as other networks, such a local area network (LAN) or wide area network (WAN).

When used in a LAN networking environment, the computer 1610 is connected to the LAN through a network interface or adapter. When used in a WAN networking environment, the computer 1610 typically includes a communications component, such as a modem, or other means for establishing communications over the WAN, such as the Internet. A communications component, such as a modem, which may be internal or external, may be connected to the system bus 1621 via the user input interface of input 1640, or other appropriate mechanism. In a networked environment, program modules depicted relative to the computer 1610, or portions thereof, may be stored in a remote memory storage device. It will be appreciated that the network connections shown and described are exemplary and other means of establishing a communications link between the computers may be used.

The present invention has been described herein by way of examples. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, for the avoidance of doubt, such terms are intended to be inclusive in a manner similar to the term “comprising” as an open transition word without precluding any additional or other elements.

Various implementations of the invention described herein may have aspects that are wholly in hardware, partly in hardware and partly in software, as well as in software. As used herein, the terms “component,” “system” and the like are likewise intended to refer to a computer-related entity, either hardware, a combination of hardware and software, software, or software in execution. For example, a component may be, but is not limited to being, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of illustration, both an application running on computer and the computer can be a component. One or more components may reside within a process and/or thread of execution and a component may be localized on one computer and/or distributed between two or more computers.

Thus, the methods and apparatus of the present invention, or certain aspects or portions thereof, may take the form of program code (i.e., instructions) embodied in tangible media, such as floppy diskettes, CD-ROMs, hard drives, or any other machine-readable storage medium, wherein, when the program code is loaded into and executed by a machine, such as a computer, the machine becomes an apparatus for practicing the invention. In the case of program code execution on programmable computers, the computing device generally includes a processor, a storage medium readable by the processor (including volatile and non-volatile memory and/or storage elements), at least one input device, and at least one output device.

Furthermore, the invention may be described in the general context of computer-executable instructions, such as program modules, executed by one or more components. Generally, program modules include routines, programs, objects, data structures, etc. that perform particular tasks or implement particular abstract data types. Typically the functionality of the program modules may be combined or distributed as desired in various embodiments. Furthermore, as will be appreciated various portions of the disclosed systems above and methods below may include or consist of artificial intelligence or knowledge or rule based components, sub-components, processes, means, methodologies, or mechanisms (e.g., support vector machines, neural networks, expert systems, Bayesian belief networks, fuzzy logic, data fusion engines, classifiers . . . ). Such components, inter alia, can automate certain mechanisms or processes performed thereby to make portions of the systems and methods more adaptive as well as efficient and intelligent.

Additionally, the disclosed subject matter may be implemented as a system, method, apparatus, or article of manufacture using standard programming and/or engineering techniques to produce software, firmware, hardware, or any combination thereof to control a computer or processor based device to implement aspects detailed herein. The terms “article of manufacture,” “computer program product” or similar terms, where used herein, are intended to encompass a computer program accessible from any computer-readable device, carrier, or media. For example, computer readable media can include but are not limited to magnetic storage devices (e.g., hard disk, floppy disk, magnetic strips . . . ), optical disks (e.g., compact disk (CD), digital versatile disk (DVD) . . . ), smart cards, and flash memory devices (e.g., card, stick). Additionally, it is known that a carrier wave can be employed to carry computer-readable electronic data such as those used in transmitting and receiving electronic mail or in accessing a network such as the Internet or a local area network (LAN).

The aforementioned systems have been described with respect to interaction between several components. It can be appreciated that such systems and components can include those components or specified sub-components, some of the specified components or sub-components, and/or additional components, according to various permutations and combinations of the foregoing. Sub-components can also be implemented as components communicatively coupled to other components rather than included within parent components, e.g., according to a hierarchical arrangement. Additionally, it should be noted that one or more components may be combined into a single component providing aggregate functionality or divided into several separate sub-components, and any one or more middle layers, such as a management layer, may be provided to communicatively couple to such sub-components in order to provide integrated functionality. Any components described herein may also interact with one or more other components not specifically described herein but generally known by those of skill in the art. 

1. A method for determining a linear precoder for multiple-input multiple-output (MIMO) channels with outdated channel state information, the method comprising: receiving an indication of outdated channel state information for MIMO channels in a wireless network; receiving an indication of a transmit power constraint for at least one consumer premise equipment (CPE); solving an optimization problem subject to the indicated power constraint, the optimization problem dependent at least in part on the outdated channel state information; and determining a linear precoder based on the solving of the optimization problem.
 2. The method of claim 1 wherein the solving of the optimization problem subject to the indicated power constraint comprises utilizing a modified gradient algorithm.
 3. The method of claim 2 wherein the solving of the optimization problem subject to the indicated power constraint further comprises performing projection to satisfy the indicated power constraint.
 4. The method of claim 1 wherein the receiving of the indication of a transmit power constraint for at least one consumer premise equipment includes receiving an indication of a transmit power constraint for multiple CPEs.
 5. The method of claim 1 wherein the determining of the linear precoder comprises indicating a linear precoder to each of multiple CPEs.
 6. The method of claim 1 wherein the solving of the optimization problem subject to the indicated power constraint comprises solving the optimization problem at a base station of the wireless network.
 7. The method of claim 1 wherein the solving of the optimization problem subject to the indicated power constraint comprises solving the optimization problem to minimize pairwise error probability.
 8. The method of claim 1 wherein the solving of the optimization problem subject to the indicated power constraint comprises solving the optimization problem at each of multiple CPEs in the wireless network.
 9. The system of claim 10 wherein the determining of the linear precoder comprises indicating a linear precoder for orthogonal space-time block code (STBC).
 10. An apparatus for determining a linear precoder for a multiple-input multiple-output wireless network, the apparatus comprising: multiple antennas; a memory; a channel state information component that receives an indication of outdated channel state information for the multiple-input multiple-output wireless network; a power constraint component that receives an indication of power constraints for at least one device in the wireless network; an optimization component that determines one or more precoders by solving an optimization problem dependent on the outdated channel state information and subject to the indicated power constraints.
 11. The apparatus of claim 10 wherein the optimization component determines a precoder for each of multiple devices in the wireless network.
 12. The apparatus of claim 10 wherein the optimization component solves the optimization problem using a gradient descent algorithm.
 13. The apparatus of claim 10 wherein the multiple antennas comprise multiple receiving antennas and multiple transmitting antennas.
 14. The apparatus of claim 10 wherein the power constraint component receives an indication of transmit power constraints from each of the devices in the wireless network.
 15. The apparatus of claim 10 further comprising a transmission component that transmits data in accordance with the determined precoder.
 16. The system of claim 10 wherein the apparatus is a consumer premise equipment.
 17. The apparatus of claim 10 wherein the apparatus is a base station.
 18. A computer-readable medium containing instructions that, when executed, performs a method for determining a linear precoder for space-time block coding (STBC), the method comprising: receiving outdated channel state information for space-time block coded MIMO channels in a wireless network; receiving transmit power constraint information associated with at least one consumer premise equipment (CPE); and subject to at least one constraint represented by the transmit power constraint information and based at least in part on the outdated channel state information, optimizing at least one parameter for a linear precoder.
 19. The computer-readable medium of claim 18 further comprising, wherein the optimizing of the at least one parameter for a linear precoder includes optimizing at least one parameter for a linear precoder for orthogonal STBC.
 20. The computer-readable medium of claim 18 wherein the optimizing comprises performing projection to satisfy the at least one power constraint. 